The experimental results demonstrate that can match or outperform previously published approaches. We extensively evaluate on FewRel1.0 and FewRel2.0 benchmarks under four few-shot configurations. A dual graph interaction mechanism is proposed to adequately fuse the information between the two graphs in a cyclic flow manner. an instance graph and a distribution graph) to explicitly model the intraclass similarity and interclass dissimilarity in each individual graph, as well as the instance-level and distribution-level relations across graphs. leverages an edge-labeling dual graph (i.e. Specifically, we propose, a novel graph neural network (GNN) based approach for few-shot relation extraction. In this work, we concentrate on few-shot relation extraction under domain adaptation settings. It is thus vital to reduce the data requirement in training and explicitly model the distribution difference when transferring knowledge from one domain to another. However, current models still suffer from two main drawbacks: 1) they require enormous volumes of training data to avoid model overfitting and 2) there is a sharp decrease in performance when the data distribution during training and testing shift from one domain to the other. Recent advances in relation extraction with deep neural architectures have achieved excellent performance. Our study calls more attentions for future research to stop simplifying problem setups, and to model DocRE in the wild rather than in an unrealistic Utopian world. Our findings reveal that most of current DocRE models are vulnerable to entity mention attacks and difficult to be deployed in real-world end-user NLP applications. We also have a close check on model usability in a more realistic setting. Next, we construct four types of entity mention attacks to examine the robustness of typical DocRE models by behavioral probing. By taking a comprehensive literature review and a thorough examination of popular DocRE datasets, we find that these performance gains are achieved upon a strong or even untenable assumption in common: all named entities are perfectly localized, normalized, and typed in advance. Instead, we take a closer look at the field to see if these performance gains are actually true. In this paper, we do not aim at proposing a novel model for DocRE. Recently, numerous efforts have continued to push up performance boundaries of document-level relation extraction (DocRE) and have claimed significant progress in DocRE.
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